anti-lock braking system (abs) is a nonlinear and time varying system including uncertainty, so it cannot be controlled by classic methods. intelligent methods such as fuzzy controller are used in this area extensively; however traditional fuzzy controller using simple type-1 fuzzy sets may not be robust enough to overcome uncertainties. for this reason an interval type-2 fuzzy controller is de...

anti-lock braking system (abs) which is a nonlinear and time variant system may not be easily controlled by classic control methods. this is due to the fact that classic linear controllers are just capable of controlling a specific plant in small region of state space. to overcome this problem, a more powerful control technique must be employed for complex nonlinear plants. fuzzy controllers ar...

Reinforcement schemes represent the basis of the learning process for stochastic learning automata, generating their learning behavior. An automaton using a reinforcement scheme can decide the best action, based on past actions and environment responses. The aim of this paper is to introduce a new reinforcement scheme for stochastic learning automata. We test our schema and compare with other n...

In this paper, a novel efficient optimization method based on reinforcement learning automata (RLA) for optimum parameters setting of conventional proportional-integralderivative (PID) controller for AVR system of power synchronous generator is proposed. The proposed method is Combinatorial Discrete and Continuous Action Reinforcement Learning Automata (CDCARLA) which is able to explore and lea...

Learning automata are reinforcement learners belonging to the class of policy iterators. They have already been shown to exhibit nice convergence properties in a wide range of discrete action game settings. Recently, a new formulation for a Continuous Action Reinforcement Learning Automata (CARLA) was proposed. In this paper we study the behavior of these CARLA in continuous action games and pr...

Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, so agents may learn a source policy in a setting where data is readily available, with the hope that it generalises well to the target domain. We propose a new multi-stage RL agent, DARLA (DisentAngled Representation Learning Agent), which learns to see befor...

A key problem in multi-agent reinforcement learning remains dealing with the large state spaces typically associated with realistic distributed agent systems. As the state space grows, agent policies become more and more complex and learning slows. One possible solution for an agent to continue learning in these large-scale systems is to learn a policy which generalizes over states, rather than...

This article considers the problem of learning the correct temporal sequence of discrete behaviors from a finite behavior set that will lead to completion of a complex task, using only stochastic reinforcement from the environment. A trial-and-error learning algorithm is proposed that is inspired by backward chaining from the animal training discipline. The procedure is analytically formulated ...

Learning automata are reinforcement learners belonging to the category of policy iterators. They have already been shown to exhibit nice convergence properties in discrete action games. Recently, a new formulation for a Continuous Action Reinforcement Learning Automaton (CARLA) was proposed. In this paper we study the behavior of these CARLA in continuous action games and propose a novel method...

A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. The aim is to design an automaton, using an evolutionary reinforcement scheme (the basis of the learning process), that can determine the best action guided by past ac...